In order to check, adjust, extend or more generally improve geographic information systems, use is made of information captured from location aware devices, such as global positioning systems and collected at a central system. Such data is often referred to as floating car data (FCD) coming from so-called probe-vehicles, i.e. vehicles that are equipped with the necessary devices to transmit data to a data center at regular time intervals. The data comprises information on the status of the vehicle, for instance its location and speed. In the data center the data are processed in order to make them useable. The accuracy of the data depends on the frequency of the positioning and broadcasting of the data, the accuracy of the GPS and the number of probe-vehicles. Based on this data, different traffic related information can be derived. Some examples are discussed below
In the Netherlands, the Ministry of Transport, Public Works and Water Management (Ministerie van Verkeer en Waterstaat) has carried out an experiment with FCD. The purpose of this experiment was to investigate the usefulness of FCD and to get an understanding of the possibilities and problems with FCD. The experiment was part of a large innovation research program called “Roads to the Future”. Approximately 60 vehicles in the city of Rotterdam were equipped with GPS and GSM devices and the data were used to estimate travel times. After the data had been filtered, about 75% of all the measurements could be used to estimate the travel times. The accuracy of the estimated travel times lies within 1% of the actual travel times for relatively larger road sections.
FCD is used in the production and maintenance of road network databases. This production process requires a lot of work and resources. Furthermore, the current digital networks have an inherent static nature while the real road networks are dynamic by nature—new roads are built and old ones reconstructed. Temporary changes such as road works and accidents also influence the network. In order to overcome this problem, it was suggested to use an algorithm that derives road networks from FCD. The idea behind this is: “where there are vehicles, there must be a road”.
A complete prototype system is known that uses FCD for both automatic and manual detection of queues in traffic. The system consists of small hardware units placed in mobile traffic report units (taxis were used) and backstage databases that collect all the data from the report units. The automatic detection was based on analyzing GPS data from the taxis. The manual detection was based on taxi drivers reporting traffic queues by using the equipment in the taxis. A one-month field test, where 10 taxis were used, showed that the system is operational and that the communication costs are very low. The field test also provoked new questions, such as how many taxis are needed to do real-time queue detection, how to combine automatic and manual queue detection, and how to integrate the FCD with existing queue detection systems.
Systems using FCD to calculate detailed routes and travel times for hazardous goods transport in the Austrian road network also are known. Furthermore the FCD are used to calculate historical time series and actual travel times.
The current state of the art systems and methods are based on vector processing of probe track geometries. This information is to be coupled to existing digital maps, representing the centerline geometry of a road segment as a widely spaced sequence of latitude and longitude vector points connected by line segments. Geometry also may be represented as a sequence of points, adding estimated standard deviations for longitude and latitude as a vector attribute to represent confidence in the point. Connecting the points by linear interpolation is sufficient for low-curvature tracks, but for roads with higher curvature higher-order interpolation is possible (e.g. spline representation).